IEEE Trans Image Process - Integrating graph partitioning and matching for trajectory analysis in video surveillance.

Tópicos

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{ model(2341) predict(2261) use(1141) }
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{ method(2212) result(1239) propos(1039) }

Resumo

In order to track moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g., 15 frames. We initially calculate a foreground map at each frame obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching. The task can be formulated by maximizing a posteriori under the Bayesian framework, in which we integrate the spatio-temporal contexts and the appearance models. The probabilistic inference is achieved by a data-driven Markov chain Monte Carlo algorithm. Given a period of observed frames, the algorithm simulates an ergodic and aperiodic Markov chain, and it visits a sequence of solution states in the joint space of spatial graph partitioning and temporal graph matching. In the experiments, our method is tested on several challenging videos from the public datasets of visual surveillance, and it outperforms the state-of-the-art methods.

Resumo Limpo

order track move object long rang occlus interrupt background clutter paper propos unifi approach global trajectori analysi instead tradit framebyfram track method recov target trajectori base short sequenc video frame eg frame initi calcul foreground map frame obtain stateoftheart background model attribut graph extract foreground map graph vertic imag primit repres composit featur graph represent pose trajectori analysi joint task spatial graph partit tempor graph match task can formul maxim posteriori bayesian framework integr spatiotempor context appear model probabilist infer achiev datadriven markov chain mont carlo algorithm given period observ frame algorithm simul ergod aperiod markov chain visit sequenc solut state joint space spatial graph partit tempor graph match experi method test sever challeng video public dataset visual surveil outperform stateoftheart method

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